2026 Summer Physicist/Scientist Intern - Undergrad (Santa Clara, CA)

Applied MaterialsSanta Clara, CA
2dOnsite

About The Position

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. Applied Materials’ Computational Products and Solutions (CPS) group is seeking a highly motivated intern to join our team in Summer 2026. Our team develops state‑of-the-art multi‑physics models of Applied Materials process chambers and collaborates closely with internal product development teams to optimize designs for next‑generation semiconductor applications. We also develop and maintain ACE+, our commercial multi‑physics simulation platform used by leading technology companies worldwide. As an intern in the Computational Products & Solutions group, you will contribute to the development and optimization of simulations and modeling tools supporting next‑generation semiconductor applications.

Requirements

  • Student must be pursuing a Bachelor’s degree program in Computer Science, Aerospace, Mechanical, Chemical, Electrical Engineering or a related field
  • Student must be in good academic standing at their university, with a preferred GPA of 3.0 or above on a 4.0 scale
  • Programming experience with Python, including implementing machine learning algorithms and building models.
  • Ability to rapidly understand new simulation and ML frameworks

Nice To Haves

  • Experience with physics simulation software preferred

Responsibilities

  • Develop pipelines for training machine learning surrogate models
  • Simulation workflow automation using internal and open-source tools
  • Perform testing and optimization of modeling workflows
  • Custom software application development
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